Global constraints and reranking have not been used in cognates detection research to date.We propose methods for using global constraints by performing rescoring of the score matrices produced by state of the art cognates detection systems.Using global constraints to perform rescoring is complementary to state of the art methods for performing cognates detection and results in significant performance improvements beyond current state of the art performance on publicly available datasets with different language pairs and various conditions such as different levels of baseline state of the art performance and different data size conditions, including with more realistic large data size conditions than have been evaluated with in the past.

Broad-coverage knowledge bases (KBs) such as Wikipedia, Freebase, Microsoft's Satori and Google's Knowledge Graph contain structured data describing real-world entities. These data sources have become increasingly important for a wide range of intelligent systems: from information retrieval and question answering, to Facebook's Graph Search, IBM's Watson, and more. Previous work on learning to populate knowledge bases from text has, for the most part, made the simplifying assumption that facts remain constant over time. But this is inaccurate -- we live in a rapidly changing world. Knowledge should not be viewed as a static snapshot, but instead a rapidly evolving set of facts that must change as the world changes.

Translation Memory (TM) systems are one of the most widely used translation technologies.An important part of TM systems is the matching algorithm that determines what translations get retrieved from the bank of available translations to assist the human translator.Although detailed accounts of the matching algorithms used in commercial systems can't be found in the literature, it is widely believed that edit distance algorithms are used.This paper investigates and evaluates the use of several matching algorithms, including the edit distance algorithm that is believed to be at the heart of most modern commercial TM systems.This paper presents results showing how well various matching algorithms correlate with human judgments of helpfulness (collected via crowdsourcing with Amazon's Mechanical Turk).A new algorithm based on weighted n-gram precision that can be adjusted for translator length preferences consistently returns translations judged to be most helpful by translators for multiple domains and language pairs.